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Bioinformatics

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Bioinformatics

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Bioinformatics

  1. 1. Bioinformatics: Applications ZOO 5903/4903 Fall 2006, MW 10:30-11:45 Sutton Hall, Room 312 Jonathan D. Wren, Ph.D.
  2. 2. Prereqs <ul><li>General knowledge of biology is required </li></ul><ul><li>Programming experience will help but is not necessary </li></ul><ul><li>Course will emphasize Internet-based applications and databases </li></ul>
  3. 3. Goals for this course <ul><li>Gain an understanding of the range of problems being tackled by bioinformatics </li></ul><ul><li>Understand what bioinformatics programs are available and being used in biomedical research </li></ul><ul><li>Be able to interpret the output & results of bioinformatics programs and understand their limitations </li></ul><ul><li>Learn more about Biology from an information standpoint </li></ul><ul><li>For those taking the advanced course, build a knowledge base </li></ul>
  4. 4. Overview <ul><li>Grade weighting </li></ul><ul><li>Homework 35% (10 total)‏ </li></ul><ul><li>First 3 exams 30% </li></ul><ul><li>Final exam 20% (comprehensive)‏ </li></ul><ul><li>Report 10% </li></ul><ul><li>Participation 5% </li></ul><ul><li>(homework is generally given on Wednesday and due the following Monday)‏ </li></ul>
  5. 5. Textbook Plus, there will be supplementary reading assignments, usually online, and a few extra (suggested) readings
  6. 6. General policy/philosophy <ul><li>Lectures complement and enhance (but don’t duplicate) reading assignments </li></ul><ul><li>Homeworks are intended to bridge theory & practice, keep you on track for exams, and permit you to go at your own pace. </li></ul><ul><li>Homeworks may be turned in late, but for every class they are late, 10% will be deducted. </li></ul><ul><li>Exams are intended to gauge synthesis of topics covered </li></ul><ul><li>I do believe in curves, but can’t guarantee their application for every grade </li></ul>
  7. 7. <ul><li>Office : Room 2025, Stephenson Center for Research and Technology </li></ul><ul><li>Hours : 1 pm – 4 pm Monday </li></ul><ul><li>1 pm – 4 pm Wednesday </li></ul>[email_address]
  8. 8. Strategic overview Instruction set  Active programs  Product  System  Network
  9. 9. Outline for 1 st exam classes (DNA thread)‏ <ul><li>Introduction to bioinformatics </li></ul><ul><li>Genomes, sequencing and features </li></ul><ul><li>Sequence alignment basics </li></ul><ul><li>Multiple sequence alignments </li></ul><ul><li>Phylogenetics – evolutionary relationships </li></ul><ul><li>Genome comparison & diversity </li></ul>
  10. 10. What is Bioinformatics? <ul><li>Development of methods & algorithms to organize, integrate, analyze and interpret biological and biomedical data </li></ul><ul><li>Study of the inherent structure & flow of biological information </li></ul><ul><li>Goals of bioinformatics: </li></ul><ul><ul><li>Identify patterns </li></ul></ul><ul><ul><li>Classify </li></ul></ul><ul><ul><li>Make predictions </li></ul></ul><ul><ul><li>Create models </li></ul></ul><ul><ul><li>Better utilize existing knowledge </li></ul></ul><ul><ul><li>“ Two months in the lab can easily save an afternoon on the computer.” –Alan Bleasby </li></ul></ul>
  11. 11. The “old” biology The most challenging task for a scientist is to get good data
  12. 12. The “new” biology The most challenging task for a scientist is to make sense of lots of data
  13. 13. Old vs New - What’s the difference? 1) Economics <ul><li>Miniaturize – less cost </li></ul><ul><li>Multiplex – more data </li></ul><ul><li>Parallelize – save time </li></ul><ul><li>Automate – minimize human intervention </li></ul><ul><li>Thus, you must be able to deal with large amounts of data and trust the process that generated it </li></ul>
  14. 14. What’s the difference? 2) Scale <ul><li>From gene sequencing (~ 1 KB) to genome sequencing (many MB, even GB)‏ </li></ul><ul><li>From picking several genes for expression studies to analyzing the expression patterns of all genes </li></ul><ul><li>From a catalog of key genes in a few key species to a catalog of all genes in many species </li></ul><ul><li>Analyzing your data in isolation makes less sense when you can make much more powerful statements by including data from others </li></ul>
  15. 15. What’s the difference? 3) Logic <ul><li>Hypothesis-driven research to data-driven research </li></ul><ul><li>Expertise-driven approach versus information-driven approach </li></ul><ul><li>Reductionist versus integrationist </li></ul><ul><li>How to answer the question becomes how to question an answer </li></ul><ul><li>Algorithmic approaches for filtering, normalizing, analyzing and interpreting become increasingly important </li></ul>
  16. 16. Data-driven Science Done Wrong <ul><li>Must have some hypothesis – data is not the end goal of science </li></ul><ul><li>Finding patterns in the data is where analysis starts, not ends </li></ul><ul><li>Must understand the limits of high-throughput technology (e.g. microarrays measure transcription only, one genome does not tell you about species variation, etc.)‏ </li></ul><ul><li>Must understand or explore the limits of your algorithm </li></ul>
  17. 17. Data is being collected faster and in greater amounts
  18. 19. Growth in information & knowledge >4,800 Journals >16,000,000 records 672,000 new papers in 2005 (~1,840 per day)‏ MEDLINE spans:
  19. 20. The use of software & algorithms is becoming more common in biomedical research
  20. 21. Bio informatics
  21. 22. The use of bioinformatics and rate of growth is field-dependant
  22. 23. Data => Information => Knowledge <ul><li>Gene X mutated </li></ul><ul><li>in disease Y </li></ul>
  23. 24. Data => Information => Knowledge <ul><li>Gene X mutated </li></ul><ul><li>in disease Y </li></ul><ul><li>Gene X unmutated </li></ul><ul><li>in normal controls </li></ul>Gene X is correlated with disease Y
  24. 25. Data => Information => Knowledge <ul><li>Gene X mutated </li></ul><ul><li>in disease Y </li></ul><ul><li>Gene X unmutated </li></ul><ul><li>in normal controls </li></ul>Gene X is correlated with disease Y Gene X homozygotes have severe disease Gene X is probably causal in disease Y
  25. 26. Bioinformatics – what can it do for you? <ul><li>Most research has a growing computational aspect to it </li></ul><ul><li>No matter what you do after graduation, being tech-savvy gives you an advantage </li></ul><ul><li>Boundaries between disciplines are blurring </li></ul>
  26. 29. For next time <ul><li>Read Mount, Chapter 2, pages 33-40 and page 45 (FASTA format)‏ </li></ul><ul><li>Read Mount, Chapter 11, pages 496-511 </li></ul>

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